TY  - JOUR
A1  - März, Juliane
A1  - Kurlbaum, Max
A1  - Roche-Lancaster, Oisin
A1  - Deutschbein, Timo
A1  - Peitzsch, Mirko
A1  - Prehn, Cornelia
A1  - Weismann, Dirk
A1  - Robledo, Mercedes
A1  - Adamski, Jerzy
A1  - Fassnacht, Martin
A1  - Kunz, Meik
A1  - Kroiss, Matthias
T1  - Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors
JF  - Frontiers in Endocrinology
N2  - Context
Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.

Objective
Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.

Design
Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.

Patients
Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.

Results
Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.
By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.

Conclusions
The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.
KW  - adrenal
KW  - pheochromocytoma
KW  - paraganglioma
KW  - targeted metabolomics
KW  - mass spectronomy
KW  - catecholamines
KW  - machine learning
KW  - feature selection
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245710
SN  - 1664-2392
VL  - 12
ER  - 
TY  - JOUR
A1  - Reel, Smarti
A1  - Reel, Parminder S.
A1  - Erlic, Zoran
A1  - Amar, Laurence
A1  - Pecori, Alessio
A1  - Larsen, Casper K.
A1  - Tetti, Martina
A1  - Pamporaki, Christina
A1  - Prehn, Cornelia
A1  - Adamski, Jerzy
A1  - Prejbisz, Aleksander
A1  - Ceccato, Filippo
A1  - Scaroni, Carla
A1  - Kroiss, Matthias
A1  - Dennedy, Michael C.
A1  - Deinum, Jaap
A1  - Eisenhofer, Graeme
A1  - Langton, Katharina
A1  - Mulatero, Paolo
A1  - Reincke, Martin
A1  - Rossi, Gian Paolo
A1  - Lenzini, Livia
A1  - Davies, Eleanor
A1  - Gimenez-Roqueplo, Anne-Paule
A1  - Assié, Guillaume
A1  - Blanchard, Anne
A1  - Zennaro, Maria-Christina
A1  - Beuschlein, Felix
A1  - Jefferson, Emily R.
T1  - Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
JF  - Metabolites
N2  - Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.
KW  - metabolomics
KW  - machine learning
KW  - hypertension
KW  - primary aldosteronism
KW  - pheochromocytoma/paraganglioma
KW  - Cushing syndrome
KW  - biomarkers
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286161
SN  - 2218-1989
VL  - 12
IS  - 8
ER  -